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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

Showing 161170 of 1918 papers

TitleStatusHype
Reward-free World Models for Online Imitation LearningCode1
Multi-Objective-Optimization Multi-AUV Assisted Data Collection Framework for IoUT Based on Offline Reinforcement Learning0
MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation0
Learning Agents With Prioritization and Parameter Noise in Continuous State and Action Space0
DIAR: Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation0
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task0
Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Search0
Online waveform selection for cognitive radar0
Asymptotic Analysis of Sample-averaged Q-learning0
Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving0
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